Senior Credit Risk Modeller
To support Credit Risk team building and supporting credit risk models used for IRB or other business needs.
This Client does not provide sponsorship.
Key Responsibilities of the Senior Credit Risk Modeller-
- To develop Credit Risk models.
- To support the wider range of statistical modelling.
- To support the development and maintenance of an IRB modelling approach for Basel II retail Credit Risk (PD, EAD, LGD).
- To enhance the modelling capabilities.
- To develop the knowledge and understanding of modelling within the business.
- To lead production of intra month forecasts taking into account trends seen over the course of the month.
- To drive medium and long term forecasting models whilst engaging all stakeholders in other business areas and ensure functional impacts are incorporated accurately and transparently.
- To lead production of variance analyses that clearly show variance vs forecast and leverage this to enhance forecasting process.
- To formulate key assumptions and drivers of impairment and present to stakeholders from Analyst to Senior Management.
- To explore new modelling approaches (clustering, segmentation, strategic analytics approaches etc.) that would enable more robust forecast.
- To adhere to all relevant regulatory rules and guidance applicable to the role.
- To maintain required standards of the Conduct Rules at all times in accordance with prevailing regulatory requirements.
- To maintain full awareness of and conformance to the Risk Management Framework including but not limited to the Information Security and Data Governance supporting frameworks.
Ideal knowledge and experience of the Senior Credit Risk Modeller-
- Previous experience within Risk, Financial industry or in a sector with exposure to relative statistical modelling.
- Analytical experience on Basel modelling (PD, EAD, LGD, Economical capital requirements etc.).
- UK banking experience.
- In-depth knowledge and understanding of statistical aspects (especially these which are used in Credit Risk-Basel modelling):
- Logistic-Linear Regression, Scorecard development; GINI coefficient; KS- statistics; Reject inference techniques; Data sampling; Data mining; Markov chains; and time series modelling approaches.
- Excellent analytical skills and a strong attention to detail.
- A degree qualification in a relevant discipline, e.g. Maths/Statistics, Economics.